DocumentCode
2252614
Title
The application of rough neural network in RMF model
Author
Wang, Wei ; Mi, Hong
Author_Institution
Dept of Autom., Xiamen Univ., Xiamen, China
Volume
1
fYear
2010
fDate
6-7 March 2010
Firstpage
210
Lastpage
213
Abstract
In many models of customer relationship management (CRM) analysis, RFM model is widely accepted. RMF model is an important tool to weigh customer value and customer profitability. To address this issue, this paper closely combines the rough set theory with neural network and uses rough set theory to process the random sample data from dataset. Then the data is projected from high-dimensional to low-dimensional, and the redundant attributes of sample data are removed. The sampling data which is processed after using rough set theory is trained on the neural network. At last, we use the test data to test and verify this model. Experimental results show that compared with the traditional BP neural network, rough neural network has a significant improvement in accuracy, and an advantage in the computing speed.
Keywords
customer relationship management; neural nets; rough set theory; CRM; RMF model; customer profitability; customer relationship management; random sample data; rough neural network; rough set theory; weigh customer value; Asia; Customer relationship management; Data mining; Decision trees; Informatics; Neural networks; Robotics and automation; Set theory; Symmetric matrices; Testing; RMF model; attribute reduction; data mining; neural network; rough set;
fLanguage
English
Publisher
ieee
Conference_Titel
Informatics in Control, Automation and Robotics (CAR), 2010 2nd International Asia Conference on
Conference_Location
Wuhan
ISSN
1948-3414
Print_ISBN
978-1-4244-5192-0
Electronic_ISBN
1948-3414
Type
conf
DOI
10.1109/CAR.2010.5456865
Filename
5456865
Link To Document